Nonlinear identification of a distillation column process is a challenging problem in the process industry. Controller performance of nonlinear and dynamic column can be viewed or analyzed using this type of identification. In this work, a novel identification method is proposed for distillation column simulated in realistic conditions using HYSYS using hybrid artificial bee colony (ABC) and artificial neural network (ANN). Since real distillation columns are dynamic in nature, this hybrid system is used as a nonlinear function in nonlinear autoregressive with exogenous input (NARX) structure. This hybrid NARX model is called NARX ANN ABC. In NARX ANN ABC, NARX ANN is trained using ABC algorithm. ABC training process benefit of training neural network without trapping at local optimal points. Reflux rate and reboiler temperature used as variable inputs while top and bottom compositions have been used as variable outputs. HYSYS software used for generating data. 1000 samples of data was collected from HYSYS. 800 samples of data was used for training, and remaining 200 samples of that was used for validation of the proposed model. The performance of proposed model has been compared with ANN, NARX ANN, and ANN ABC. The result showed NARX ANN ABC outperformed others.
OsuolaleF. and ZhangJ., Distillation control structure selection for energy-efficient operations, Chemical Engineering & Technology38 (2015), 907–916. doi: 10.1002/ceat.201400707
2.
Mohamed RamliN., HussainM.A., Mohamed JanB. and AbdullahB., Composition prediction of a debutanizer column using equation based artificial neural network model, Neurocomputing131 (2014), 59–76. doi: 10.1016/j.neucom.2013.10.039
3.
MacmurrayJ.C. and HimmelblauD.M., Modeling and control of a packed distillation column using artificial neural networks, Computers & Chemical Engineering19 (1995), 1077–1088. doi: 10.1016/0098-1354(94)00098-9
4.
Fernandez de CaneteJ., Garcia-CerezoA., Garcia-MoralI., Del SazP., and OchoaE., Object-oriented approach applied to ANFIS modeling and control of a distillation column, Expert Systems with Applications40 (2013), 5648–5660. doi: 10.1016/j.eswa.2013.04.012
5.
Fernández de Cañete,J., CorderoT., GuijasD., and AlarconJ., An adaptive neuro-fuzzy approach to control a distillation column, Neural Computing & Applications9 (2000), 211–217. doi: 10.1007/s005210070014
6.
MiccioM. and CosenzaB., Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers, Journal of Process Control24 (2014), 475–484. doi: 10.1016/j.jprocont.2013.12.007
7.
RaniA., SinghV. and GuptaJ.R.P., Development of soft sensor for neural network based control of distillation column, ISA Transactions52 (2013), 43849–doi: 10.1016/j.isatra.2012.12.009
8.
MahindrakarV. and HahnJ., Dynamics and control of benzene hydrogenation via reactive distillation, Journal of Process Control24 (2014), 113–124. doi: 10.1016/j.jprocont.2014.01.005
9.
AraromiD.O., SonibareJ.A., and EmuoyibofarheJ.O., Fuzzy identification of reactive distillation for acetic acid recovery from waste water, Journal of Environmental Chemical Engineering2 (2014), 1394–1403. doi: 10.1016/j.jece.2014.05.008
10.
BachnasA.A., TóthR., LudlageJ.H.A. and MesbahA., A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study, Journal of Process Control24 (2014), 272–285. doi: 10.1016/j.jprocont.2014.01.015
11.
RamchandranS. and RhineharttR.R., A very simple structure for neural network control of distillation, Journal of Process Control5 (1995), 115–128.
12.
LuoR.F., ShaoH.H. and ZhangZ.J., Fuzzy-neural-net-based inferential control for a high-purity distillation column, Control Engineering Practice3 (1995), 31–40. doi: 10.1016/0967-0661(94)00061-K
13.
Fernandez de CaneteJ., Del Saz-OrozcoP., Garcia-MoralI., and Gonzalez-PerezS., Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant, Applied Soft Computing12 (2012), 2728–2739. doi: 10.1016/j.asoc.2012.03.062
14.
RasoulifardM.H., Seyed DorrajiM.S., Amani-GhadimA.R., and Keshavarz-babaeinezhadN., Visible-light photocatalytic activity of chitosan/polyaniline/CdS nanocomposite: Kinetic studies and artificial neural network modeling, Applied Catalysis A: General514 (2016), 60–70. doi: 10.1016/j.apcata.2016.01.002
15.
DuttaP. and RhinehartR.R., Application of neural network control to distillation and an experimental comparison with other advanced controllers, ISA Transactions38 (1999), 251–278. doi: 10.1016/S0019-0578(99)00025-7
.
16.
Ochoa-EstopierL.M., JobsonM. and SmithR., Operational optimization of crude oil distillation systems using artificial neural networks, Computers & Chemical Engineering59 (2013), 178–185. doi: 10.1016/j.compchemeng.2013.05.030
17.
TorrecillaJ.S., AragonJ.M. and PalancarM.C., Optimization of an artificial neural network by selecting the training function. Application to olive oil mills waste, Industrial and Engineering Chemistry Research47 (2008), 7072–7080. doi: 10.1021/ie8001205
18.
EberhartR. and KennedyJ., A new optimizer using particle swarm theory. MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science 19995, 3943. doi: 10.1109/MHS.1995.494215
19.
KarabogaD., AkayB. and OzturkC., Artificial Bee Colony (ABC) optimization algorithm for training feed-forward neural networks, 4617 (2007), 318–329. doi: 10.1007/978-3-540- 73729-2
20.
MavrovouniotisM. and YangS., Training neural networks with ant colony optimization algorithms for pattern classification, Soft Computing19 (2015), 1511–1522. doi: 10.1007/s00500-014-1334-5
21.
KalinliA., SagirogluS. and SarikocF., Parallel ant colony optimization algorithm based neural method for determining resonant frequencies of various microstrip antennas, Electromagnetics30 (2010), 463–481. doi: 10.1080/02726343.2010.483939
22.
WhitleyD., StarkweatherT. and BogartC., Genetic algorithms and neural networks: Optimizing connections and connectivity, Parallel Computing14 (1990), 347–361. doi: 10.1016/0167- 8191(90)90086-O
23.
JonesA., Genetic algorithms and their applications to the design of neural networks, Neural Computing & Applications1 (1993), 32–45. doi: 10.1007/BF01411373
24.
TsaiJ.T., ChouJ.H. and LiuT.K., Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm, IEEE Transactions on Neural Networks17 (2006), 69–80. doi: 10.1109/TNN.2005.860885
25.
ZhangC., ShaoH., LiY., Particle swarm optimisation for evolving artificial neural network, SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions (Cat. No. 00CH37166), 4 (2009), 2487–2490. doi: 10.1109/ICSMC.2000.884366
.
26.
Van den BerghF., and EngelbrechtA., Cooperative learning in neural networks using particle swarm optimizers, South African Computer Journal26 (2000), 84–90.
27.
MeissnerM., SchmukerM. and SchneiderG., Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training, BMC Bioinformatics7 (2006), 125–doi: 10.1186/1471-2105-7-125
28.
Escamilla-SalazarI.G., Torres-Trevi noL., and Gonzalez-OrtizB., Intelligent parameter identification of machining Ti64 alloy, The International Journal of Advanced Manufacturing and Technology86(5-8) (2016), 1997–2009. doi: 10.1007/s00170-015-7967-4
29.
NasimiR. and IraniR., Identification and modeling of a yeast fermentation bioreactor using hybrid particle swarm optimization-artificial neural networks, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects36 (2014), 1604–1611. doi: 10.1080/15567036.2011.592903
30.
BlumC. and SochaK., Training feed-forward neural networks with ant colony optimization: An application to pattern classification. Hybrid Intelligent Systems, 2005. HIS ’05. IEEE, Fifth International Conference on (2005), 233–238. doi: 10.1109/ICHIS.2005.104
31.
Pourkamali AnarakiM., and SadeghiM., Honey bee-inspired algorithms for SNP haplotype reconstruction problem, Journal of Experimental & Theoretical Artificial Intelligence (2015), 114–doi: 10.1080/0952813X.2015.1020525
32.
KarabogaD., GorkemliB., OzturkC. and KarabogaN., A comprehensive survey: Artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review42 (2014), 21–57. doi: 10.1007/s10462-012-9328-0
33.
KarabogaD. and BasturkB., A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization39 (2007), 459–471. doi: 10.1007/s10898-007-9149-x
34.
KarabogaD. and BasturkB., On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing Journal8 (2008), 687–697. doi: 10.1016/j.asoc.2007.05.007
35.
KarabogaD. and AkayB., A comparative study of Artificial Bee Colony algorithm, Applied Mathematics and Computation214 (2009), 108–132. doi: 10.1016/j.amc.2009.03.090
36.
BullinariaJ., AlYahyaK., Artificial Bee Colony Training of Neural Networks, In: TerrazasG., OteroF.E.B., MasegosaA.D., (Eds.), Nature Insired Cooperative Strategies for Optimization (NICSO 2013) SEâĂ Ş15. Springer International Publishing. 2014, 191–201. doi: 10.1007/978-3-319-01692-4_15
37.
KisiO., OzkanC., and Akay. Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm, Journal of Hydrology (2012), 94–103. doi: 10.1016/j.jhydrol.2012.01.026
38.
AwanS.M., AslamM., KhanZ.A. and SaeedH., An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting, Neural Computing and Applications25(7-8) (2014), 1967–1978. doi: 10.1007/s00521-014-1685-y
39.
UzluE., AkpınarA., ÖturkH.T., NacarS. and KankalM., Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey, Energy69 (2014), 638–647. doi: 10.1016/j.energy.2014.03.059
40.
WangS., ZhangY., DongZ., DuS., JiG., YanJ. and PhillipsP., Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection, International Journal of Imaging Systems and Technology25 (2015), 153164. doi: 10.1002/ima.22132
41.
BoztoprakH., Özbay,Y., GüçlüD. and KüçükhemekM., Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant, Desalination and Water Treatment3994 (2015), 111. doi: 10.1080/19443994.2015.1085909
42.
LuybenW.L., Plantwide dynamic simulators in chemical pocessing and control. CRC Press, 2002.
43.
KarabogaD., An idea based on honey bee swarm fo numerical optimization. Technical Report-TR06, Kayseri, Turkey: Erciyes University, 2005.
44.
LjungL., System Identification Theory for the User, Prentice Hall, Englewood Cliffs, NJ, 1987.
45.
Ravi SriniwasG., ArkunY., Chien, I-Lung, Ogunnaike and BubatundeA., Nonlinear identification and control of a high-purity distillation column: A case study, Journal of Process Control5 (1995), 149–162.
46.
JaleelA., AparnaK., Identification of ethane-ethylene distillation column using neural network and anfis, in 2015 Fifth International Conference on Advances in Computing and Communications (ICACC), pp. 358–361, IEEE.
47.
JaleelA., AparnaK., Identification of benzene-toluene distillation column using neuro-fuzzy algorithm, in 2015 Annual IEEE India Conference (INDICON), pp. 1–6, IEEE, 2015. doi: 10.1109/ICACC.2015.17
48.
AbdulE., Jaleel and K. Aparna, Neuro-fuzzy soft sensor estimator for benzene toluene distillation column, Procedia Technology25 (2016), 92–99. doi: 10.1016/j.protcy.2016.08.085
